pdh course g383 - pdhonline.compdhonline course g383 (2 pdh) vector analysis 2012 instructor: mark...

PDHonline Course G383 (2 PDH)

Vector Analysis

2012

Instructor: Mark A. Strain, P.E.

PDH Online | PDH Center5272 Meadow Estates Drive

Fairfax, VA 22030-6658Phone & Fax: 703-988-0088

www.PDHonline.orgwww.PDHcenter.com

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Table of Contents Introduction..................................................................................................................................... 1 Vector Decomposition .................................................................................................................... 1

Cartesian Coordinate System...................................................................................................... 1 Components of a Vector ................................................................................................................. 3 Properties of a Vector ..................................................................................................................... 6

Addition ...................................................................................................................................... 6 Commutative Property ............................................................................................................ 6 Associative Property ............................................................................................................... 6

Scalar Multiplication................................................................................................................... 6 Commutative Property ............................................................................................................ 6 Associative Property ............................................................................................................... 7 Distributive Property............................................................................................................... 7 Identity Vector and Zero Vector ............................................................................................. 7

Dot Product ..................................................................................................................................... 7 Applications of the Dot Product ................................................................................................. 9

Angle formed Between Two Vectors ..................................................................................... 9 Projection of a vector onto a Line........................................................................................... 9

Cross Product .................................................................................................................................. 9 Computation of the Cross Product............................................................................................ 11

Cross Product by Multiplying Components.......................................................................... 11 Cross Product by Matrix Method.......................................................................................... 12 Properties of the cross product.............................................................................................. 12

Triple Product ........................................................................................................................... 13 Summary ....................................................................................................................................... 13 References..................................................................................................................................... 14

©2012 Mark A. Strain Page ii of 16


Introduction Mechanics is the science of motion and the study of the action of forces on bodies. Mechanics is a physical science incorporating mathematical concepts directly applicable to many fields of engineering such as mechanical, civil, structural and electrical engineering.

Vector analysis is a mathematical tool used in mechanics to explain and predict physical phenomena. The word “vector” comes from the Latin word vectus (or vehere – meaning to carry). A vector is a depiction or symbol showing movement or a force carried from point A to point B.

A scalar is a quantity, like mass (14 kg), temperature (25°C), or electric field intensity (40 N/C) that only has magnitude and no direction. On the other hand, a vector has both magnitude and direction. Physical quantities that have magnitude and direction can be represented by the length and direction of an arrow. The typical notation for a vector is as follows:

or simply A

Note: vectors in this course will be denoted as a boldface letter: A.

Figure 1 – Illustration of a vector

Vectors play an important role in physics (specifically in kinematics) when discussing velocity and acceleration. A velocity vector contains a scalar (speed) and a given direction. Acceleration, also a vector, is the rate of change of velocity.

Vector Decomposition

Cartesian Coordinate System Consider a 3-dimensional Cartesian coordinate system:

©2012 Mark A. Strain Page 1 of 16

A


Figure 2 - Cartesian (or rectangular coordinate system)

A Cartesian (or rectangular) coordinate system has three mutually perpendicular axes: x, y and z. A vector in this coordinate system will have components along each axes.

A unit vector is a vector along an axes (x, y or z) with a length of one. Let the unit vector along the x-axis be i and the unit vector along the y-axis be j and the unit vector along the z-axis be k. The Cartesian coordinate system with three unit vectors is shown in Figure 3.

Figure 3 - Rectangular coordinate system showing unit vectors



A vector can connect two points in space as in Figure 4.

Figure 4 - Vector connecting two points in space

Components of a Vector In a Cartesian coordinate system the components of a vector are the projections of the vector along the x, y and z axes. Consider the vector A. The vector A can be broken down into its components along each axis: Ax, Ay and Az in the following manner:

A = Ax i + Ay j + Az k Note that the vectors i, j and k are the unit vectors along each corresponding axis. The unit vectors i, j and k each have a length of one, and the magnitudes along each direction are given by Ax, Ay and Az.

Figure 5 - Vector decomposition showing components along each axis


Trigonometry is utilized to compute the vector components Ax, Ay and Az. Consider a vector in 2-dimensional space:


Figure 6 - Vector in 2-dimensional space

The components of this 2-dimensional vector are computed with respect to the angle θ as follows:

Ax = Acosθ and

Ay = Asinθ Where A is the magnitude of A given by

A = Ax2 + A

y2

For example, let

A = 5 and θ = 36.8° then

Ax = 5cos(36.8°) = 5(0.8)

= 4

and

Ay = 5sin(36.8°) = 5(0.6)

= 3

Therefore, the vector (in rectangular form) is

A = 4i + 3j

As a result of the Pythagorean Theorem from trigonometry the magnitude of a vector may be calculated by


A = Ax2 + A

y2 + A

z2


The magnitude may also be denoted as

A = │A│ or A = ║A║ The magnitude of a vector is the length of the vector. It is a scalar (length only) with no direction. In physics, for example, speed is a scalar and velocity is a vector, so speed is the magnitude of the velocity vector.

Now consider, once again, the vector in 3-dimensional space:

Figure 7 - Vector decomposition showing angles to axes

The components of this 3-dimensional vector are computed with respect to the angles θx, θy and θz as follows:

Ax = Acosθx Ay = AcosθyAz = Acosθz

where A = A

x2 + A

y2 + A

z2

is the magnitude of A and

cos2θx + cos2θy + cos2θz = 1.

A unit vector can be constructed along a vector by dividing the vector by its magnitude. The result is a vector along the same direction as the original vector with magnitude 1. Consider the unit vector a:



This is also called vector normalization. The magnitude of a is one.

Properties of a Vector

Addition Vector addition is accomplished by adding the components (Ax, Ay and Az) of one vector to the associated components (BBx, ByB and BBz) of another vector:

A + B = (Ax + BBx)i + (Ay + ByB )j + (Az + BBz)k For example, consider the following vectors A and B:

A = i + 2j + 5k B = 3i + j + 2k

then

A + B = (1 + 3)i + (2 + 1)j + (5 + 2)k = 4i + 3j + 7k

Commutative Property Vector addition follows the commutative property:

A + B = B + A

Associative Property Vector addition also follows the associative property:

(A + B) + C = A + (B + C)

Scalar Multiplication Vectors can be multiplied by real numbers (called scalars). To accomplish this the vector components (Ax, Ay and Az) are each multiplied by the real number (n):

nA = nAxi + nAyj + nAzk For example, let n = 5, and

A = i + 2j + 5k then

nA = 5i + 10j + 25k

Commutative Property Scalar multiplication of a vector follows the commutative property:


nA = An


(where n is a scalar)

Associative Property Scalar multiplication of a vector follows the associative property:

(ab)A = a(bA) = (ba)A = b(aA)

(where a and b are scalars)

Distributive Property Scalar multiplication of a vector follows the distributive property:

(a + b)A = aA + bA a(A + B) = aA + aB

(where a and b are scalars)

Identity Vector and Zero Vector 1A = A

0 + A = A

Dot Product The dot product (or scalar product, or inner product) is a vector operation that takes two vectors and generates a scalar quantity (a single number). The dot product is used to obtain the cosine of the angle between two vectors.

The dot product is denoted by the following notation:

A • B Figure 8 shows two vectors in space with an angle θ between the two vectors:

Figure 8 - Dot product of two vectors

The dot product is defined by

A • B = ABcosθ


where


A = Ax2 + A

y2 + A

z2

and

B = Bx2 + B

y2 + B

z2

and θ is the angle between the two vectors.

The dot product between two vectors A and B is also defined as A • B = AxBBx + AyByB + AzBBz

or generally as

A • B = ΣAiBBi(where i is defined from 1 to n, where n is

the number of dimensions)

The above definition is derived from the following expansion, since the base unit vectors are orthogonal:

A • B = (Ax i + Ay j + Az k) • (BBx i + By B j + BBz k) = AxBBx i • i + AxByB i • j + AxBBz i • k + AyBBx j • i + AyByB j • j + AyBBz j • k + AzBBx k • i + AzByB k • j + AzBBz k • k

Since the base unit vectors i, j and k are orthogonal (or perpendicular): i • i = 1 i • j = 0 j • j = 1 i • k = 0 k • k = 1 j • k = 0

then the dot product reduces to

A • B = AxBBx + AyByB + AzBBz

For example, let

A = 3i + 5j + k and

B = 2i + 3j – 4k then the dot product of the two vectors is

A • B = 6 + 15 – 4 = 17



Applications of the Dot Product

Angle formed Between Two Vectors To find the angle formed by two vectors use the definition of the dot product:

A • B = ABcosθ and

A • B = AxBBx + AyByB + AzBBzSetting these two equalities equal to each other provides the following result:

ABcosθ = AxBBx + AyByB + AzBBzor

cosθ = AxBx + AyBy + AzBz AB

Projection of a vector onto a Line Consider a vector P forming an angle θ with a line. The projection of P on the line is also called the orthogonal projection.

Figure 9 - Projection of a vector onto a line

The projection of P on line L is given by R = Pcosθ

Cross Product


The cross product (or vector product, or outer product) of two vectors results in a vector perpendicular to both vectors. The magnitude of the resulting vector is equal to the area of the parallelogram generated by the two vectors. The area of a parallelogram equals the height times the base, which is a magnitude of the cross product.


Figure 10 - Cross product of two vectors

The cross product is denoted by

A × B The cross product is defined by

A × B = ABsinθ n

(where θ is the angle between the vectors and n is the unit vector normal or perpendicular to A and B)

The name cross product is derived from the cross symbol “×” that is used to designate its operation. The name vector product emphasizes the vector nature of the result, instead of a scalar. The cross product has many applications in mathematics, physics and engineering such as the moment of a force about a point (or torque).

Figure 11 - Moment (M) of a force (F) about a point O


The cross product obeys the right-hand rule as in Figure 12.


Figure 12 - Right-Hand rule

The right-hand rule is used to determine the direction of the resulting vector. The vectors A and B form a plane. The vector formed from the resulting cross product A × B is in a direction perpendicular to the plane of the two vectors. Using the right-hand rule will determine if the direction of the vector is above the plane of the vectors or below the plane. Using your right hand, place your hand above the plane of the vectors at their vertex. Curl your fingers in the direction from A to B. If necessary, turn your hand over so that your thumb points down through the plane in order to curl your fingers from A to B. The resulting vector points in the direction of your thumb, either up or down.

Computation of the Cross Product The cross product may be computed by multiplying the components of the vectors or by assembling the components along with their unit vectors into a matrix and taking the determinant of the matrix.

Cross Product by Multiplying Components A × B = (Ax i + Ay j + Az k) × (BBx i + By B j + BBz k)

Consider the cross product of the unit vectors i, j and k i × i = 0 i × j = k j × j = 0 j × k = i k × k = 0 k × i = j

j × i = –k i × k = –j k × j = –i

Therefore,

A × B = (Ax i + Ay j + Az k) × (BBx i + By B j + BBz k) = AxBBx i×i + AxByB i×j + AxBBz i×k + AyBBx j×i + AyByB j×j + AyBBz j×k + AzBBx k×i + AzByB k×j + AzB


Bz k×k


= AxBBx (0) + AxByB k + AxBBz (–j) + AyBBx (–k) + AyByB (0) + AyBBz i +

AzBBx j + AzByB (–i) + AzBBz (0) = (AyBBz – AzByB ) i + (AzBBx – AxBzB ) j + (AxBBy – AyBxB ) k

or, graphically, and perhaps easier to remember:

Using this method, take two consecutive unit vectors and their cross product is the next vector. For example, i × j = k and j × k = i. Going to the right (in the direction of the arrow) the resulting vector is positive. Alternatively, going to the left (against the arrow) the resulting vector is positive. For example, j × i = –k and i × k = –j.

Cross Product by Matrix Method

or

A × B = (AyBBz – AzByB ) i + (AzBBx – AxBzB ) j + (AxBBy – AyBxB ) k For example, let

A = 2i – 3j + 5k and

B = –i + 2j + 4k Then the cross product of the two vectors is

= 12i – 5j + 4k – (3k + 10i + 8j) = –22i – 13j + k

Properties of the cross product Anti-Commutative

A × B = –B × A Scalar Multiplication


nA × B = n(A × B)


Vector Addition and the Cross Product

(A + B) × C = A × C + B × C and

A × (B + C) = A × B + A × C

Triple Product The triple product is the dot product of a vector with the result of the cross product of two other vectors.

A • (B × C) The triple product may be computed by taking the determinant of the following matrix:

The following triple products are equal

A • (B × C) = B • (C × A) = C • (A × B)

Summary Vector mechanics is the application of vectors in the science of mechanics. Mechanics is the science of motion and the study of the action of forces on bodies. Vector analysis is very important in many fields of engineering such as mechanical, civil, structural and electrical engineering.

Scalar values, such as mass and temperature convey only a magnitude, but vectors such as velocity employ both a magnitude and a direction. The dot product is a vector operation on two vectors that produces a scalar value. The dot product is used to find the angle between two vectors or to find the projection of a vector onto a line. The cross product is a vector operation on two vectors that produces another vector. The cross product may be used to calculate the moment of a force around a point at a given radius.



References

1. Beer, Ferdinand P. and Johnston, E Russell Jr. Vector Mechanics for Engineers: Statics, Fifth Edition. New York, New York: McGraw-Hill Book Company, 1988.

2. “Cross Product”. 24 January 2012

3. Davis, Harry F. and Snider Arthur David. Introduction to Vector Analysis, Seventh Edition. Dubuque, IA: Wm C. Brown Publishers: 1995.

4. “Dot Product”. 30 December 2011

5. “Euclidean Vector”. 26 January 2012

6. “Review A: Vector Analysis”. visited 27 December 2011

7. “Vector Methods”. visited 27 December 2011


http://en.wikipedia.org/wiki/Cross_producthttp://en.wikipedia.org/wiki/Dot_producthttp://en.wikipedia.org/wiki/Euclidean_vectorhttp://web.mit.edu/8.02t/www/materials/modules/ReviewA.pdfhttp://emweb.unl.edu/math/mathweb/vectors/vectors.html

Introduction Vector Decomposition Cartesian Coordinate System Components of a Vector Properties of a Vector Addition Commutative Property Associative Property

Scalar Multiplication Commutative Property Associative Property Distributive Property Identity Vector and Zero Vector

Dot Product Applications of the Dot Product Angle formed Between Two Vectors Projection of a vector onto a Line

Cross Product Computation of the Cross Product Cross Product by Multiplying Components Cross Product by Matrix Method Properties of the cross product

Triple Product

Summary References

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